Method and device for calculating a biological component density of a subject

ABSTRACT

A method for calculating a biological component density of this invention comprises steps of irradiating a light of NIR spectrum to a skin of a subject, receiving the light of NIR reflected from the skin to obtain NIR spectrum data thereof, and substituting the NIR spectrum data into a predetermined calibrating equation to obtain a biological component density of the subject such as glucose density. This invention is characterized by preparing a plurality of the calibrating equations which are different from each other and are specific to each of plural groups which are classified in terms of a skin thickness parameter indicative of a skin thickness with respect to individuals of a species to which the subject belongs, determining the skin thickness parameter of the subject with a non-invasive technique and identifying the group of the subject in accordance with the determined skin thickness parameter, and deriving one of the calibrating equations in match with the identified group in order to calculate the biological component density of the subject.

TECHNICAL FIELD

The present invention relates to a method and a device for calculating abiological component density of a subject and, more particularly, itrelates to a method and a device for calculating a biological componentdensity for calculating a chemical component in body tissue byspectrochemical analysis using absorption of light in near-infraredregion. Specifically, it relates to a useful method and a device tocalculate glucose density in skin structure based on quantitativeanalysis.

BACKGROUND ART

Japanese Non-examined Patent Publication No.2000-131322 discloses aquantitative method of glucose density and a device for the method. Thisprior art comprises the steps of irradiating light of near infrared(hereinafter called NIR) spectrum to a skin of a subject, receiving thelight of NIR reflected from the skin to obtain NIR spectrum datathereof, and substituting the NIR spectrum data into a predeterminedcalibrating equation to obtain glucose density of the subject. In thisprior art, when a biological component density in skin structure of eachsubject is measured, different calibrating equations are prepared foreach individual to measure it, because there are large individualdifferences in skin structure of human beings or living things assubjects.

However, preparing the calibrating equations for each individual or eachmeasurement part requires to restrain people or living things asmeasuring objects for a long time to prepare the calibrating equationsby multivariate analysis of the NIR spectrum data and an actualmeasurement data. Therefore, the subjects and those who prepare thecalibrating equations have to have heavy burden. For this reason, it wasdifficult to carry out easy and correct measurement to each of subjectswith big individual differences.

DISCLOSURE OF THE INVENTION

In view of the above problem, the object of the present invention is toprovide a method and a device for calculating a biological componentdensity in skin structure of subjects with large individual differenceswithout preparing a different calibrating equation for each individual.

A method for calculating a biological component density of thisinvention comprises steps of irradiating a light of NIR spectrum to askin of a subject, receiving the light of NIR reflected from the skin toobtain NIR spectrum data thereof, and substituting the NIR spectrum datainto a predetermined calibrating equation to obtain a biologicalcomponent density of the subject such as glucose density. This inventionis characterized by preparing a plurality of the calibrating equationswhich are different from each other and are specific to each of pluralgroups which are classified in terms of a skin thickness parameterindicative of a skin thickness with respect to individuals of a speciesto which the subject belongs, determining the skin thickness parameterof the subject with a non-invasive technique and identifying the groupof the subject in accordance with the determined skin thicknessparameter, and deriving one of the calibrating equations in match withthe identified group in order to calculate the biological componentdensity of the subject. Consequently, what is necessary is just toprepare the calibrating equations for each of the groups, not for eachindividual, which are classified in terms of a skin thickness parameter.So it is possible to calculate the biological component density simplyand accurately to many individuals.

It is preferable that the skin thickness parameter is determined byanalyzing the NIR spectrum reflected from the skin of the subjectstatistically. Consequently, it is possible to use the reflected NIRspectrum which is used in order to calculate the glucose densityeffectively to classify the subjects in terms of the skin thicknessparameter without using another equipment. It is preferable to use aprincipal component analysis as the statistical analysis.

Instead of the statistical analysis, it is also effective that the NIRspectrum reflected from the skin of the subject is analyzed with respectto an absorption coefficient of the spectrum at a frequency at which thespectrum is expected to show specific absorption due to existence ofsubcutaneous fat of the individual, thereby determining the skinthickness parameter based upon the absorption coefficient. That is, itbecomes possible to classify the individual easily based of the skinthickness by using the fact that the amount of the subcutaneous fatobtained by analysis of the absorption coefficient has a correlationwith the skin thickness. It is preferable that the frequency at whichthe absorption coefficient is measured is within a range of 1700 nm to1800 nm.

Furthermore, in a preferred embodiment of this invention, the NIRspectrum is irradiated to the skin selectively through one of aplurality of different incident paths which are spaced by differentdistances along a skin surface from a common reflective path throughwhich the NIR spectrum is reflected out from the skin. The differentincident paths are assigned as being specifically suitable to thegroups, respectively. The NIR spectrum irradiated through one of theincident paths and reflected from the skin is analyzed to determine theskin thickness parameter, and one of the different incident pathsassigned to one of the groups identified by the determined skinthickness parameter is selected. The selected incident path is madeactive to irradiate the NIR spectrum to the skin so as to obtain the NIRspectrum data reflected from the skin. The NIR spectrum data isprocessed by use of the calibrating equation specific to one of thegroups determined by the skin thickness parameter. The above proceduretakes it into consideration that the position of a dermal organization,which is supposed to reflect most the biological component to becalculated, changes with the skin thickness, therefore, it becomespossible to select the reflective path which certainly passes the dermalorganization and calculate the correct biological component density bypreparing two or more reflective paths according to the skin thickness,namely, according to the groups.

Instead of the above statistical analyzing and the above analyzingmethod to analyze the reflected NIR spectrum with respect to theabsorption coefficient at certain frequency, the skin thicknessparameter may be determined by a non-invasive technique using anultrasound thickness gauge or an optical coherence tomography.

A device for realizing the above mentioned method comprises a lightsource generating a light having a NIR spectrum, an incident guidedirecting the light to a skin of the subject, a reflective guidedirecting the NIR spectrum reflected back from within the skin, a sensorreceiving the NIR spectrum through the reflective guide to provide NIRdata thereof, and a processing unit which substitutes the NIR data intoa predetermined calibrating equation to calculate the biologicalcomponent density such as glucose density of the subject. The devicefurther includes a skin thickness memory storing a plurality of thecalibrating equations which are different from each other and which areeach specific to each of plural groups classified in terms of a skinthickness parameter indicative of the skin thickness with respect toindividuals of a species to which the subject belongs, and a means fordetermining the skin thickness parameter with an non-invasive techniqueto identify the group of the subject in accordance with the determinedskin thickness parameter. The above processing unit derives one of thecalibrating equations from the skin thickness memory in match with theidentified group, and calculate the biological component density of thesubject.

It is preferable that the incident guide has a light projecting endadapted to be held in close proximity to the skin, and the reflectiveguide has a light receiving end adapted to be held in close proximity tothe skin, and the light receiving end is spaced from the lightprojecting end by a distance of 2 mm or less across the skin. By suchdisposition, it becomes possible to obtain the NIR spectrum which passedand reflected subsequently the dermal organization which is supposed toreflect most the biological component to be calculated. Therefore, themeasurement of the biological component density can be made accurately.

In a preferred embodiment, the incident guide and the reflective guideare made respectively by optical fibers, and they are integrated into asingle probe head having an object end to which the light projecting endand the light receiving end are exposed. Consequently, the operation tothe skin of the subject becomes easy.

It is also preferable that the prove includes a plurality of differentincident guides and a single reflective guide, and the differentincident guides have individual light projecting ends which are spacedby different distances, respectively from the light receiving end. Thelight source is selectively coupled with one of the different incidentguides by a selector, and the NIR spectrum is irradiated through theselected incident guide to the skin, and the reflected NIR spectrumtherefrom is sent out to the above sensor. The processing unit furtherincludes a table storing a relation between each one of the groups andeach one of the different incident guides and a module which analyzedthe NIR data statistically based upon the NIR spectrum irradiatedthrough one of the incident paths and received from the skin todetermine the skin thickness parameter and to identify the correspondingone of the groups. The module selects from the table one of thedifferent incident guides as corresponding to the identified group, andenables the selector to activate the selected incident guide to directthe NIR spectrum to the skin for calculation of the biological componentdensity based upon the NIR spectrum irradiated through the selectedincident path and reflected from the skin. Therefore, it becomespossible to take out the NIR spectrum which passes the dermalorganization of which depth varies by the skin thickness and reflectssubsequently, as effective data, and thereby, it becomes possible tomeasure the biological component density accurately based on the data.

It is preferable that a plurality of the incident guides are arrangedaround the single reflective guide and the light projecting ends aredisposed coaxially around the light receiving end, on the object end ofthe probe head. Thereby, the NIR spectrum reflected from the skin can bedirected certainly to the single light receiving end.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic view of a device for calculating a biologicalcomponent density in accordance with one embodiment of the invention.

FIG. 2 is a block diagram showing the device of FIG. 1.

FIG. 3 is a block diagram showing a substantial part of the device ofFIG. 2.

FIG. 4 is a schematic view showing basic principle of the measurement ofthe biological component density using the above device.

FIG. 5 is a view showing a end face of the probe used for the device.

FIG. 6 is a flow chart showing a measurement procedure of the biologicalcomponent density using the above device.

FIG. 7 is a graph showing NIR spectrum measured on skin of a medial sideof a forearm of a subject using the device.

FIG. 8 is a graph showing a relation between a wavelength of NIRspectrum and a regression coefficient for preparing a calibratingequation used for the device based on a statistics procedure.

FIG. 9 is a graph showing absorption spectrums of typical biologicalcomponents.

FIG. 10 is a graph showing a relation between an estimate and an actualmeasurement when glucose density which changes as time go on wasmeasured using the device.

FIGS. 11A-11D are graphs showing a relation between an estimate and anactual measurement of the glucose density, respectively, when subjectswhose skin thickness are different from each other were measured by onecalibrating equation using the above device.

FIG. 12 is a graph showing a correlation between an estimate and anactual measurement of the glucose density, when subjects whose skinthickness are different from each other were measured by one calibratingequation using the above device.

FIG. 13 is a graph showing a correlation between an estimate and anactual measurement of the glucose density, when subjects who belong toone group with the same skin thickness are measured by one calibratingequation in match with the group using the device.

FIG. 14 is a graph showing a correlation between an estimate and anactual measurement of the glucose density, when subjects who belong toother group with the same skin thickness are measured by one calibratingequation in match with the group using the device.

FIG. 15 is a graph showing a result of principal component analysis ofthe NIR spectrum reflected from a skin.

FIG. 16 shows loading plots of a second principal component of the aboveprincipal component analysis.

FIG. 17 is a flow chart showing a method for calculating the biologicalcomponent density in accordance with a second embodiment of theinvention.

FIG. 18 is a view showing an end face of a probe used for the abovemethod.

FIG. 19 is a flow chart showing a method for calculating the biologicalcomponent density in accordance with a third embodiment of theinvention, and

FIG. 20 is a flow chart showing a method for calculating the biologicalcomponent density in accordance with a fourth embodiment of theinvention.

BEST MODE FOR CARRYING OUT THE INVENTION

FIG. 1 and FIG. 2 show a device for calculating a biological componentdensity in accordance with a first embodiment of the present invention.This device comprises a light source 20 which is a halogen lamp, a lightprojecting lens group 30 which condenses a light having a NIR spectrumfrom the light source 20, a probe 40 for irradiating the light whichpassed the light projecting lens group 30 to a standard board 1 and to askin 10 of a subject and for receiving the light reflected diffuselytherefrom, a condenser lens group 50 which condenses the light from theprobe 40, a diffraction grating 60 for dispersing the NIR spectrum whichpassed the condenser lens group 50, an array sensor 70 for detecting theNIR spectrum dispersed by the diffraction grating 60, and a processingunit 100 which processes the electrical signal indicative of the NIRspectrum from the array sensor 70 thereby calculating the biologicalcomponent density. In this embodiment, glucose density is calculated asan example of the biological component density by the device. As thearray sensor 70, An InGaAs array type photo detector is used. As theprocessing unit 100, a personal computer is used.

The prove 40 comprises a reference probe and a measurement probe whichare made respectively by optical fibers. Each probe comprises a incidentguide 41 or 42 which directs the light from the light source 20 to thestandard board 1 or the skin 10 of the subject and a reflective guide 43or 44 which directs the reflected light from the standard board 1 or theskin 10 to the diffraction grating 60. One end of each incident guideand one end of each reflective guide are built in object heads 45 or 46,which are placed opposite the standard board 1 made of ceramic or theskin 10 of the subject, respectively. Both probes are integrated into alight receiving head 47 at each end which receives the light from thelight source 20, and the light receiving head 47 is connected to thelight projecting lens group 30. Each probe is connected to an outputhead 48 or 49 at each end which outputs the light to the diffractiongrating 60, and each of the output heads is connected to the condenserlens group 50, respectively, through a shutter 51 or 52.

Hereinafter, a measurement principle of the glucose density using theabove device will be described briefly. First, the object head 45 isheld in proximity to the standard board 1, and the shutter 51 is opened,and the array sensor 70 receives the reflected light (reference signal)from the standard board 1. Then, the object head 46 is brought intocontact with the surface of the skin 10 of the subject with a contactpressure of 9.8-49 kPa (100-500 gf/cm²), preferably a fixed pressure of29.4 kPa (300 gf/cm²), and the shutter 52 is opened, and the arraysensor 70 receives the NIR spectrum (biological signal) reflecteddiffusely within the skin structure through the reflective guide 44. Theprocessing unit 100 processes the obtained reference signal and theobtained biological signal thereby calculating the glucose density ofthe subject.

In order to determine the glucose density, a suitable calibratingequation is selected from two or more calibrating equations preparedbeforehand, and is used. Each of the calibrating equations is differentfrom each other according to the skin thickness of the subject and isprepared for each of plural groups in which skin thickness of thesubject are different from each other. Each of the calibrating equationsis calculated by a spectral analysis method by multivariate analysis, inwhich glucose density of which quantity is determined by a normalprocess is used as a response variable and body tissue spectrum obtainedby this spectral analysis device is used as an explanatory variable. Asthe multivariate analysis, a multiple regression analysis, PLSregression analysis, a neural network, etc. can be applied.

FIG. 3 shows various kinds of function modules realized by a programperformed in the above processing unit 100 and a table which stores datarequired for the modules in a memory. This table 110 constitutes a skinthickness memory storing a plurality of the calibrating equations(calibrating equation 1 and calibrating equation 2 in this embodiment)which are each specific to each of the plural groups (group A and groupB in this embodiment) classified in terms of a skin thickness parameterindicative of the skin thickness with respect to individuals of aspecies to which the subject belongs. As the function modules, a skinthickness decision module 101, a group decision module 102, acalculation module 103, and a display module 104 are prepared. The skinthickness decision module 101 receives digital data which was convertedby A/D converter 72 from the NIR spectrum outputted from the arraysensor 70, and decides the skin thickness therefrom. The group decisionmodule 102 identifies the group from the decided skin thickness andselects the calibrating equation in match with this identified group.The calculation module 103 substitutes the NIR spectrum data into theselected calibrating equation and calculates the glucose density. Thedisplay module 104 displays the calculated glucose density on a display120.

As shown in FIG. 5, the probe used in this embodiment is designed sothat the incident guides 42 irradiating the light from the light sourceto the skin will be arranged coaxially around the reflective guide 44which takes out the reflected light from the skin to the array sensorside, on an end face of the object heads 46. As the incident guides, twokinds of guides, which have different distances from the reflectiveguide, are used. One kind is first incident guides 42-1 spaced by adistance L of 650 μm from the reflective guide, and the other kind issecond incident guides 42-2 spaced by a distance L of 300 μm from thereflective guide. There are twelve first incident guides 42-1 and sixthsecond incident guides. The first and second incident guides arealternatively selected according to the determined skin thickness forthe reason which will be stated later. In the table 110 shown in FIG. 3,an instruction for deciding which incident guide to select according tothe skin thickness, namely, according to the selected group, is stored,and the above module 102 selects the incident guide according to thegroup selected from the skin thickness determined first based on thereflected spectrum from the skin. And the module 102 issues aninstruction to a switching signal generating module 106, and a switchingsignal from the module 106 operates a selector 80 to select either thefirst incident guide or the second incident guide. And then, the glucosedensity is calculated using the NIR spectrum irradiated from theselected incident guide.

FIG. 6 is a flow chart showing the measurement procedure of the glucosedensity in this embodiment briefly. At first, the NIR spectrum reflectedfrom the skin of the subject is preliminarily measured under ameasurement condition where the first incident guide is used and thedistance L is therefore 650 μm. Then, the skin thickness is determinedby a principal component analysis, and the group corresponding to theskin thickness is determined, and the calibrating equation in match withthe determined group is selected. When the group A in which the skinthickness is 1.2 mm or more is selected, the glucose density iscalculated using the already obtained NIR spectrum. When the group B inwhich the skin thickness is less than 1.2 mm is selected, the reflectedspectrum is measured again using the second incident guide 44-2, and theglucose density is calculated base on the spectrum. This reason will beexplained later.

Although the distance L between the incident guide 42 and the reflectiveguide 44 on the object head 46 can be selected from values other thanthe above, it is desired that the distance is selected from the range of0.2 mm-2 mm. The skin structure of living things, including humanbeings, consists of three layers, the epidermis 12 including the stratumcorneum, the dermis 13, the subcutaneous tissue 14, as shown in FIG. 4.The thickness of the epidermis 12 is about 0.2-0.4 mm, the thickness ofthe dermis 13 is about 0.5-2 mm, and the thickness of the subcutaneoustissue 14 is about 1-3 mm. It is though that the glucose density in thedermis (dermal organization) 13 will follow the glucose density in bloodand will change therewith, because blood capillaries, etc. are developedin the dermis 13 and mass transfer according to the blood glucose willoccur promptly. In the subcutaneous tissue 14, fat tissue forms thebackbone, and glucose, which is water soluble, can not exist evenly inthe subcutaneous tissue layer (subcutaneous tissue) 14 easily.Therefore, in order to measure the glucose density in blood with highprecision, it is necessary to measure the NIR spectrum of the dermis 13selectively. In order to catch the NIR spectrum which passed the dermis13 and reflected diffusely, the distance L between the end of theincident guide 42 and the end of the reflective guide 44 is set to 0.2mm-2 mm. That is, as shown in FIG. 4, used is a property that the NIRspectrum irradiated from the end of the incident guide 42 penetrates theinside of the skin structure and diffuses, and the backscattering lightreaches the reflective guide 44 of the object head. This NIR spectrumtakes a U-shaped route shown in FIG. 4, and the depth of the path in theskin structure changes with the distance L. By setting the distance L tothe range of 0.2 mm-2 mm, It becomes possible to measure selectively theNIR spectrum reflecting the dermis 13 of the skin structure.

Next, the preparation of the calibrating equation will be explained.FIG. 7 is a graph showing the NIR spectrum measured by the above deviceabout subjects (P1, P5), and FIG. 8 is a graph showing the regressioncoefficient for determining the calibrating equation. In the graph shownin FIG. 8, an experiment which fluctuates the glucose densityartificially by giving a glucose load via a oral route to one subjectwas done six times, and one calibrating equation was calculated bymultivariate analysis using the NIR spectrum of the skin obtained in theexperiment and the data of glucose density measured from blood. As themultivariate analysis, PLS regression analysis was used in this case.The subject was a man, who was 41 years old and healthy. The measurementof the NIR spectrum was done on the skin of the medial side of the leftforearm of the subject, and the skin thickness of the measured portionwas about 1.5 mm in the sum of the thickness of the epidermis 12 and thedermis 13. The measurement of the skin thickness was done by using theCORTEX (Denmark) ultrasonic fault measuring device “DermaScan C Ver. 3”.

The calibrating equation for determining the glucose density will beexpressed by the following formula.

$\begin{matrix}{{{Glucose}\mspace{14mu}{density}} = {{\Sigma\;{{ai} \cdot {xi}}} + b}} \\{= {{{a1} \cdot {x1}} + {{a2} \cdot {x2}} + \ldots + {{an} \cdot {xn}} + b}}\end{matrix}$

-   -   an: Regression coefficient at wave length n,    -   xn: Absorption coefficient at wave length n, and    -   b: Constant

Since the wave length important for the decision of the glucose densityhas a large regression coefficient, the wave length important fordetermining the quantity of the glucose density can be presumed by theregression coefficient.

By the way, it is known that a glucose molecule shows unique absorptionat wave length of near 1600 nm as compared with other biologicalcomponent, as shown in the absorption spectrum of the biologicalcomponent of FIG. 9. Therefore, it is proved that the regressioncoefficient which has a characteristic peak at wave length of near 1600nm in the graph of FIG. 8 reflects the glucose density in the bodytissue. In FIG. 9, the maximum absorption coefficient of various kindsof biological components are standardized to 1.

The prove 40 used in order to determine the above calibrating equationhas twelve incident guides 42 disposed around the reflective guide 44 atthe same distance L (=650 μm), and the calibrating equationcorresponding to the subject who belongs to the group in which the skinthickness is 1.5 mm is determined by measuring the subject whose skinthickness is 1.5 mm in the total of the epidermis 12 and the dermis 13using the prove 40.

Even if a calibrating equation prepared for a certain individual issuitable for that individual, the calibrating equation can notnecessarily be applied to other individuals as it is, because there areindividual differences in the skin thickness. However, even if there areindividual differences, it is expected that a calibrating equation whichis generalized to some extent can be prepared by preparing a calibratingequation catching changes of the biological component (glucose), becausethe biological component aiming at determining the quantity (in thiscase, glucose) is common irrespective of the individual.

Next, shown in FIGS. 10 and 11 are results (estimates) of changes ofglucose density which were estimated to five subjects P1-P5 includingthe subject P1, using the calibrating equation which was prepared, asstated above, for the subject P1, and actual changes (actualmeasurements) of glucose density measured from blood, respectively. Theattribute of these five subjects are shown in Table 1.

TABLE 1 Skin Health thickness Sex Age condition (mm) Subject P1 man 41good 1.3-1.5 P2 man 37 good 1.2-1.4 P3 man 47 good 0.9-1.0 P4 man 27good 1.0-1.1 P5 man 35 good 0.8-0.9

It became clear from the graph of FIG. 10 that when the change of theglucose density of the subject P1 was estimated by the calibratingequation prepared from the data of the subject P1, a good estimation inwhich the correlation coefficient between actual measurement and theestimate is about 0.9 is possible.

Moreover, it became clear from the FIG. 11 that when the change of theblood sugar level of other subjects (P2-P5) were estimated by thecalibrating equation prepared for the subject P1, a good estimation ispossible to the subject P2 but is difficult to other subjects P3-P5. Thegraph of FIG. 12 shows a relation between the actual measurements andthe estimates in the above case where the change of the blood sugarlevel of five subjects (P1-P5) were estimated by one calibratingequation prepared for the subject P1. In this case, the correlationcoefficient is 0.69, so it is understood that the correlationcoefficient becomes low when the calibrating equation peculiar to onesubject (P1) is applied to the measurements of glucose density of otherfour persons.

Next, calibrating equations were prepared to each subject P2-P5,respectively, based on data of actual glucose density which weremeasured from blood after the glucose load experiment done six times toeach of the subjects P2-P5, respectively. More particularly, the glucoseload experiments were done six times to each subject, and sixcalibrating equations (CE1-CE6) were prepared from data of six kinds ofcombinations (C1-C6) including five experiments out of six experiments.And, using each of the calibrating equations, the glucose density of theremaining experiment out of the combination was estimated, and thecorrelation between the actual measurement and the estimate wasexamined. And, as shown in Table 2, by substituting the NIR spectrumdata obtained from the glucose load experiment done for every subjectinto the six calibrating equations prepared for each of the subjects,the glucose density of all the subjects were measured (estimated)mutually, and thereby the reliability of the estimates were examined.

TABLE 2 Subjects P1 P2 P3 P4 P5 Group A Calibrating P1 ∘ ∘ x Δ x (T ≧1.2 mm) equations P2 ∘ ∘ ∘ x x Group B P3 x Δ ∘ ∘ ∘ (T < 1.2 mm) P4 x x∘ ∘ ∘ P5 x x x x x

In this Table 2, if the average of the correlation coefficients betweenthe six estimates which were calculated about each of the subjects andcorresponding actual measurements is 0.7 or more, a “◯” mark isinscribed, and if the average is 0.6 or more and less than 0.7, a “Δ”mark is inscribed, and if the average is less than 0.6, a “x ” mark isinscribed. As understood from Table 2, in this experiment in which fivesubjects were used, these subjects are roughly divided into twoattributes (Group A, Group B), and, in each group, the glucose densitycan be estimated (measured) accurately with each other by thecalibrating equation prepared mutually. These groups A and B areclassified according to the skin thickness, as is clear from Table 1.Each of the skin thickness in Table 1 was calculated by the average ofthe skin thickness (the sum of the epidermis and the dermis) of threeplaces chosen arbitrarily from a ultrasound tomogram.

It is turned out from Table 2 that the calibrating equation peculiar tothe subject P5 is not effective in measurement of its own glucosedensity, and the calibrating equations peculiar to the subject P3 and P4who belong to the same group B are effective in measurement of theglucose density of the subject P5. This is attributed to the fact thatthe skin thickness of the subject P5 is quite thin. Summarizing theabove, the group classified according to the skin thickness is dividedinto the group A in which the skin thickness is 1.2 or more and thegroup B in which the skin thickness is less than 1.2 and, as for thegroup B in which the skin thickness is thin, the calibrating equationsprepared for the subjects whose skin thickness are 0.9 mm-1.1 mm areeffective.

As is clear from the above experiment result, the glucose density can bemeasured accurately without complicated processes of preparingcalibrating equations for each subject, if a patient or a subject, whois a measuring object of the biological component, is classifiedaccording to the skin thickness and the calibrating equations areprepared for each of the classified groups, not for each individual,beforehand.

As the calibrating equation, two kinds of calibrating equations areprepared beforehand and are stored in the table 110 in the processingunit 100. One is a calibrating equation 1, which estimates the glucosedensity of subjects whose skin thickness, the sum of the epidermis andthe dermis, are 1.2 mm or more, and the other is a calibrating equation2, which estimates the glucose density of subjects whose skin thicknessare less than 1.2 mm.

The calibrating equation 1 is expressed as follows;Glucose density (mg/dl)=a ₁₄₂₀ ·x ₁₄₂₀ +a ₁₄₂₃ ·x ₁₄₂₃ +. . . +a ₁₈₃₇ ·x₁₈₃₇ +a ₁₈₄₀ x ₁₈₄₀ +b

-   -   a_(n): Regression coefficient at wave length n,    -   x_(n): Absorption coefficient at wave length n, and    -   b: Constant

The calibrating equation 2 is expressed as follows;Glucose density (mg/dl)=c ₁₄₂₀ ·x ₁₄₂₀ +c ₁₄₂₃ ·x ₁₄₂₃ +. . . +c ₁₈₃₇ ·x₁₈₃₇ +c ₁₈₄₀ ·x ₁₈₄₀ +d

-   -   c_(n)°: Regression coefficient at wave length n,    -   x_(n): Absorption coefficient at wave length n, and    -   d: Constant

In this way, by selecting the calibrating equation according to thegroups classified in terms of the skin thickness, the reliablemeasurements in which the correlation coefficient between the actualmeasurement and the estimate is high, for example, 0.93 or 0.85 as shownin FIGS. 13 and 14, became possible.

Moreover, in this embodiment, since the first incident guide 42-1 andthe second incident guide 42-2 are switched according to the skinthickness of the subject in order to change the distance L between theincident light and the reflected light, the U-shaped optical path shownin FIG. 4 can reach the dermal organization certainly, whereby thereliability of the measurement can be raised. That is, the distance L ismade small for the group with thin skin thickness to form the opticalpath in a shallow area of the skin and to catch the dermis.

In this embodiment, the data of the NIR spectrum reflected from the skinis effectively used in order to measure the skin thickness, in additionto the measurement of the glucose density. The NIR spectrum outputtedfrom the array sensor 70 is sent to the processing unit 100 as digitaldata through A/D converter 72, and is analyzed based on a principalcomponent analysis in the skin thickness decision module 101 provided inthe processing unit 100, and is classified into the group A in which theskin thickness is 1.2 mm or more and the group B in which the skinthickness is less than 1.2 mm. FIG. 15 shows the result of theclassifying of the subjects based on the principal component analysis.As is clear form the FIG. 15, the group A in which the skin thickness,the sum of the epidermis and the dermis, is 1.2 mm or more and the groupB in which the skin thickness is less than 1.2 mm are clearly classifiedby a second principal component (PC2). FIG. 16 shows loading plots ofthe second principal component, in which there are characteristic peaksat a wavelength of 1728 nm and 1769 nm, and these peaks correspond tothe peaks of the neutral fat shown in FIG. 9. Therefore, magnitude ofcontribution of the second principal component can be interpreted as thereach of the light to the subcutaneous tissue (fat tissue), therefore,the principal component analysis can be used for the classification ofthe skin thickness as an alternative characteristic of the skinthickness. That is, the individual difference of the skin thicknessbecomes the difference of the reach of the light to the subcutaneoustissue, and appears as a configuration difference of the NIR spectrum inthe end. By classifying the configuration difference of the NIR spectrumusing the principal component analysis, the classification of thesubject based on skin thickness became possible. Besides the principalcomponent analysis, qualitative analysis such as cluster analysis anddiscriminant analysis can be used for the classification based on themultivariate analysis. In this way, the measurement of the skinthickness can be attained by software for analyzing the reflectedspectrum from the skin, and therefore, it is not necessary to useanother hardware to measure the skin thickness. So, the device can besimplified.

The switch between the two kinds incident guides, the first incidentguide 42-1 and the second incident guide 42-2, arranged as shown in FIG.5, is done by the selector 80 provided in the path from the lightprojecting lens group 30 to the probe 40. That is, the light from thelight source 20 is led to the incident guide 42-1 or 42-2 alternativelyby the selector 80. This selector 80 includes a light volume regulatorand two mechanical shutters. The light volume regulator adjusts theamount of the incident lights to the first incident guide 42-1 and theamount of one to the second incident guide 42-2, at a rate of 2:1. Theselector 80 is operated by a signal from the switching signal generatingmodule 106 realized in the processing unit 100 to validate either thefirst incident guide or the second incident guide. That is, when thedistance L1 of 650 μm is selected, the mechanical shutter on a side ofthe first incident guide 42-1 is opened and the shutter on a side of thesecond incident guide 42-2 is closed.

The measurement conditions of the glucose density of last time, such asa calculated skin thickness, etc., are stored in a measurement historytable 112 in the processing unit 100, and if a subject is fixed to thesame people as the last time, the procedure of determining the skinthickness can be skipped after the first measurement by taking out theskin thickness from the measurement history table 112. So, themeasurement can be shortened.

Although the above embodiment shows the example in which two groups Aand B, two calibrating equations 1 and 2, and two distances L1 and L2are prepared according to the skin thickness, the present invention isnot limited to the above, and it is possible to use three or moregroups, and three or more calibrating equations and distancescorresponding to the groups.

The present invention is not limited to the above embodiment, thedistance between the incident guide and the reflective guide on the endface of the object head 46 may be fixed to 650 μm, as a secondembodiment shown in FIGS. 17 and 18. In this embodiment, reliablemeasurements of the glucose density is also possible by the sameprocedure as the above embodiment, i.e., first, the skin thickness ofthe subject is measured by the principal component analysis of thereflective spectrum from the skin, which is also used for themeasurement of the glucose density, and the calibrating equation inmatch with the group corresponding to the skin thickness is selected.

FIG. 19 shows a third embodiment of the present invention, in which, theskin thickness is determined by using a ratio of an absorptioncoefficient of the NIR spectrum reflected from the skin of the subjectat a wavelength of 1728 μm (Ab1728) to an absorption coefficient at awavelength of 1670 μm (Ab1670), in order to select the group classifiedin terms of the skin thickness. That is, when “Ab1728/Ab1670>α(α:constant)”, then the skin thickness is judged to be less than 1.2 mm,and when “Ab1728/Ab1670≦α”, then the skin thickness is judged to be 1.2mm or more. After that, like the above embodiment, the glucose densityis measured by substituting the NIR spectrum data into the calibratingequation in match with the selected group. In this embodiment, the probeof which distance between the incident guide and the reflective guide is650 μm is used.

In this way, the group can be selected based on the skin thickness usingthe unique absorption wave length of the NIR spectrum caused by the fatcomponent, on a ground that the amount of subcutaneous fat isproportional to the skin thickness (reaching depth to the dermis). Inthis case, too, like the above embodiment, the skin thickness can becalculated by the non-invasive technique without adding anotherhardware.

The present invention is not limited only to the above embodiment, it isalso possible to use an ultrasonic tomogram device thereby determiningthe skin thickness of the subject, as a fourth embodiment shown in FIG.20. The skin thickness can be measured by using the CORTEX (Denmark)ultrasonic fault measuring device “DermaScan C Ver. 3”.

Besides the ultrasonic tomogram device as a means of measuring the skinthickness, an ultrasonic measurement method which does not use atomogram (fault image) may be used. Also, an optical coherencetomography may be used for the measurement of the skin thickness.Typical examples of the optical coherence tomography are a low coherenceinterferometry and a optical frequency scanning method. Both of themethods can measure a fault of a living body with a resolution of dozensmicrometer order. And, the skin thickness can be measured from atomogram of the skin obtained after irradiating NIR laser light to amedial side of the forearm.

It is also possible to classify the groups in more detail and preparecalibrating equations for each of the groups, based on indexes forclassifying the subject's attributes, such as an amount of keratinmoisture of a skin, a moisture density of skin structure, a density ofskin structure, a color of a skin, a surface roughness of a skin, sex,age, and a race, in addition to the skin thickness. In this case, moreprecise measurements of the glucose density to a variety of subjects isexpected.

Although the glucose was taken as an example of a biological componentto be measured in the above embodiments, this invention is notnecessarily limited to this and can be used for a quantitative analysisof a biological component such as an amount of organization moisture,neutral fat, cholesterol, HbA1c (saccharification hemoglobin),fructosamine, albumin, globulin, uric acid, etc. Furthermore, thisinvention can be used for measurements of a degree of skin health, skinage, a degree of aging, and a tension of a skin, etc. which are,respectively, alternative characteristics of the above biologicalcomponents.

1. A device for calculating a biological component density of a subject,which comprises: a light source generating a light having a NIR (nearinfrared) spectrum; an incident guide directing said light to a skin ofsaid subject; a reflective guide directing the NIR spectrum reflectedback from within said skin; a sensor receiving said NIR spectrum throughsaid reflective guide to provide NIR data thereof; a processing unitwhich substitutes said NIR data into a predetermined calibratingequation to calculate the biological component density of said subject,characterized in that said device further includes: a skin thicknessmemory storing a plurality of the calibrating equations which aredifferent from each other and which are each specific to each of aplural groups classified in terms of a skin thickness parameterindicative of a skin thickness with respect to individuals of a speciesto which said subject belongs; and a means for determining the skinthickness parameter with a non-invasive technique to identify the groupof the subject in accordance with the determined skin thicknessparameter; said processing unit operating to derive one of saidcalibrating equations from said skin thickness memory in match with theidentified group in order to calculate the biological component densityof the subject, wherein said skin thickness memory stores twocalibrating equations which are different from each other, onecorresponding to a group in which the skin thickness is 1.2 mm or more,and the other corresponding to a group in which the skin thickness isless than 1.2 mm, and wherein said incident guide has a light projectingend adapted to be held in close proximity to said skin, and saidreflective guide has a light receiving end adapted to be held in closeproximity to said skin, said light receiving end being spaced from saidlight projecting end by a distance of 2 mm or less across said skin,said incident guide and said reflective guide being made respectively byoptical fibers which are integrated into a single probe head having anobject end to which said light projecting end and said light receivingend are exposed, said probe including a plurality of different incidentguides and a single reflective guide, said different incident guideshaving individual light projecting ends which are spaced by differentdistances, respectively from said light receiving end on said objectend, a selector being provided to selectively couple one of saiddifferent incident guides to said light source so as to direct saidlight having the NIR spectrum through the selected incident guide, saidprocessing unit further including: a table storing a relation betweeneach one of said groups and each one of said different light incidentguides; and a module which analyzes said NIR data statistically basedupon the NIR spectrum irradiated through one of said incident paths andreceived from said skin to determine said skin thickness parameter andto identify the corresponding one of said group, said module operatingto select from said table one of said different incident guides ascorresponding to the identified group, said module calculating saidbiological component density based upon said NIR spectrum when theselected incident light guide corresponds with the incident guide whichdirected the light having the NIR spectrum to the skin to determine theskin thickness parameter, said module enabling said selector to activatethe other incident guide and directing the light having the NIR spectrumto said skin once again through the activated incident guide andcalculating said biological component density based upon the NIRspectrum received from the skin when the selected incident guide doesnot correspond with the incident guide which directed the light havingthe NIR spectrum to the skin to determine the skin thickness parameter.2. The device as set forth in claim 1, wherein a plurality of saidincident guides having the light projecting ends spaced by the samedistance from said light receiving end are arranged about said singlereflective guide to have the light projecting ends coaxial with saidlight receiving end.
 3. The device as set forth in claim 1, wherein thebiological component density is glucose density.
 4. A method ofcalculating a biological component density of a subject, said methodcomprising the steps of: irradiating a light of NIR (near infrared)spectrum to a skin of the subject; receiving said light of NIR reflectedfrom said skin to obtain NIR spectrum data thereof; substituting saidNIR spectrum data into a predetermined calibrating equation to obtain abiological component density of said subject; said method furtherincluding the step of: preparing a plurality of the calibratingequations which are different from each other and are each specific toeach of a plural groups which are classified in terms of a skinthickness parameter indicative of a skin thickness with respect toindividuals of a species to which said subject belongs; determining saidskin thickness parameter of the subject with a non-invasive techniqueand identifying the group of the subject in accordance with thedetermined skin thickness parameter, deriving one of said calibratingequations in match with the identified group in order to calculate thebiological component density of the subject, wherein said NIR spectrumis irradiated to said skin selectively through one of a plurality ofdifferent incident paths which are spaced by different distances along askin surface from a common reflective path through which said NIRspectrum is reflected out from the skin, said different incident pathsbeing assigned as being specifically suitable to said groups,respectively, said NIR spectrum irradiated through one of said incidentpaths and reflected from said skin being analyzed to determine said skinthickness parameter in order to select one of said different incidentpaths assigned to one of said groups identified by the determined skinthickness, said NIR spectrum data being processed by use of thecalibrating equation specific to one of said groups determined by saidskin thickness parameter, when the selected incident path correspondswith the incident path which directed the light having the NIR spectrumto the skin to determine the skin thickness parameter, said selectedincident path being made active to direct said light having the NIRspectrum to said skin so as to obtain said NIR spectrum data from saidNIR spectrum reflected from said skin, said NIR spectrum data beingprocessed by use of the calibrating equation specific to one of saidgroups determined by said skin thickness parameter, when the selectedincident path does not correspond with the incident path which directedthe light having the NIR spectrum to the skin to determine the skinthickness parameter.
 5. The method as set forth in claim 4, wherein saidskin thickness parameter is determined by statistically analyzing saidNIR (near infrared) spectrum reflected from the skin of the subject. 6.The method as set forth in claim 5, wherein said NIR (near infrared)spectrum is analyzed based upon a principal component analysis.
 7. Themethod as set forth in claim 4, wherein the biological component densityis glucose density.